Generally, multisource classifiers (identifiers) may be used, e.g., for Speech Signal Enhancement (SSE) purposes, to help determine which input sources should be used in Automatic Speech Recognition (ASR), and which should be excluded. For instance, the principle may be to have some core localizer that delivers the raw localization data (for instance “steered response power”). The raw data may then be classified into different classes corresponding to the sources. Existing methods for this may represent the raw input data in every frame (e.g., 10-20 ms) as a Gaussian Mixture model (GMM). This GMM may be initialized to have many classes (sources) and then the number of classes is shrunk iteratively. The commonly used algorithm for fitting a GMM is the “Expectation Maximization” algorithm (EM). Due to the shrinking, many iterative steps are generally required, which makes it computationally more demanding, and the fitting algorithm may restart in every frame.